Front Oncol. 2026 Mar 4;16:1771313. doi: 10.3389/fonc.2026.1771313. eCollection 2026.

ABSTRACT

N⁶-methyladenosine (m⁶A), as the most abundant RNA epitranscriptional modification in eukaryotes, its key component of the methyltransferase complex, METTL14, not only cooperates in catalyzing m⁶A deposition but also has functions independent of methyltransferase activity. This article systematically reviews the dual regulatory role of METTL14 in tumors and its molecular mechanisms, mainly organizing the relevant research in a logical sequence of "tumor suppressive effect - tumor promoting effect - controversial or context-dependent". Studies have shown that METTL14 often plays a tumor suppressive role in tumors such as hepatocellular carcinoma and colorectal cancer, while in pancreatic cancer and nasopharyngeal carcinoma, it mostly promotes malignant progression, showing a high degree of context dependence. This article focuses on two key mechanisms: on the one hand, METTL14 precisely regulates the processing, stability, and function of non-coding RNAs (including miRNAs, lncRNAs, and circRNAs) through m⁶A modification, reshaping the competitive endogenous RNA (ceRNA) network; on the other hand, it shapes an immunosuppressive tumor microenvironment by directly upregulating immune checkpoints such as PD-L1, mediating metabolism-immune interactions, and regulating the function of immune cells. Its functional duality also stems from the selective regulation of key pathways such as PI3K/AKT, as well as the differential interpretation by different m⁶A readers (such as YTHDF2 and IGF2BPs). Given the close association of these mechanisms with clinical prognosis, the expression level of METTL14 shows significant potential as a prognostic marker and therapeutic target; in the future, it is necessary to combine single-cell multi-omics and other technologies to analyze its dynamic regulatory network in specific tumor contexts and explore precise treatment strategies based on synthetic lethality or targeting downstream effector molecules.

PMID:41858346 | PMC:PMC12995618 | DOI:10.3389/fonc.2026.1771313

Gut. 2026 Mar 30:gutjnl-2025-337938. doi: 10.1136/gutjnl-2025-337938. Online ahead of print.

ABSTRACT

BACKGROUND: Survival outcomes after transarterial chemoembolisation (TACE) vary in hepatocellular carcinoma (HCC) patients, and existing prognostic scores and imaging models often lack generalisability and biological interpretability.

OBJECTIVE: To develop and validate a multimodal prognostication model for HCC that allows for a precise assessment of survival outcomes of HCC patients receiving TACE therapy.

DESIGN: This study enrolled 1448 HCC patients, including a TACE cohort (n=1349), a biomarker subset from a randomised trial (n=41), a single-cell RNA sequencing cohort and The Cancer Genome Atlas (TCGA) HCC cohort (n=50). Pre-treatment contrast-enhanced CT images were used to construct deep learning and conventional radiomic models. The early-fusion and late-fusion models (LFMs) were compared, and a clinical-radiologic model (CRM) was formed by integrating the better-performing LFM with clinical variables. Using TCGA data and single-cell transcriptomic profiles, the differences between high-score and low-score groups in tumour immune microenvironment, cellular functional states and key signalling pathways were investigated.

RESULTS: The CRM effectively stratified patients' survival across multiple independent cohorts and achieved more granular risk stratification than the existing clinical models. Multi-omic analyses revealed that in the LFM high-score group, myelocytomatosis oncogene was activated, epithelial-mesenchymal transition enhanced, glycolysis upregulated and hypoxia pathway activated. Single-cell transcriptomic data confirmed that virtually all cell types in high-risk patients scored high in hypoxia, and cytotoxic T cells had a reduced cytotoxic activity.

CONCLUSION: The CRM model can non-invasively predict the prognosis of HCC patients treated by TACE therapy.

PMID:41856522 | DOI:10.1136/gutjnl-2025-337938